Overview

Dataset statistics

Number of variables29
Number of observations144482
Missing cells141395
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.8 MiB
Average record size in memory397.5 B

Variable types

Numeric14
Categorical13
Text2

Alerts

STATENAME has a high cardinality: 51 distinct valuesHigh cardinality
FUNC_SYS is highly overall correlated with FUNC_SYSNAME and 2 other fieldsHigh correlation
FUNC_SYSNAME is highly overall correlated with FUNC_SYS and 1 other fieldsHigh correlation
MAN_COLL is highly overall correlated with MAN_COLLNAME and 3 other fieldsHigh correlation
MAN_COLLNAME is highly overall correlated with MAN_COLLHigh correlation
PERMVIT is highly overall correlated with MAN_COLL and 2 other fieldsHigh correlation
PERNOTMVIT is highly overall correlated with WRK_ZONENAMEHigh correlation
ROUTE is highly overall correlated with FUNC_SYSHigh correlation
RUR_URBNAME is highly overall correlated with FUNC_SYS and 1 other fieldsHigh correlation
STATENAME is highly overall correlated with ST_CASEHigh correlation
ST_CASE is highly overall correlated with STATENAMEHigh correlation
VE_FORMS is highly overall correlated with MAN_COLL and 2 other fieldsHigh correlation
VE_TOTAL is highly overall correlated with MAN_COLL and 2 other fieldsHigh correlation
WRK_ZONENAME is highly overall correlated with PERNOTMVITHigh correlation
RELJCT2NAME is highly imbalanced (57.5%)Imbalance
RUR_URBNAME is highly imbalanced (56.0%)Imbalance
TYP_INTNAME is highly imbalanced (66.9%)Imbalance
REL_ROADNAME is highly imbalanced (60.5%)Imbalance
SCH_BUSNAME is highly imbalanced (97.1%)Imbalance
WEATHERNAME is highly imbalanced (60.6%)Imbalance
WRK_ZONENAME has 141395 (97.9%) missing valuesMissing
PERNOTMVIT has 111306 (77.0%) zerosZeros
PVH_INVL has 140430 (97.2%) zerosZeros
HOUR has 5671 (3.9%) zerosZeros
MINUTE has 6865 (4.8%) zerosZeros
MAN_COLL has 87097 (60.3%) zerosZeros
ROUTE has 3519 (2.4%) zerosZeros

Reproduction

Analysis started2025-12-06 17:11:25.255651
Analysis finished2025-12-06 17:11:50.634376
Duration25.38 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

ST_CASE
Real number (ℝ)

High correlation 

Distinct40979
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272164.45
Minimum10001
Maximum560121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size705.6 KiB
2025-12-06T17:11:50.714509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile40885.05
Q1121848.25
median260876
Q3420573.75
95-th percentile510551.95
Maximum560121
Range550120
Interquartile range (IQR)298725.5

Descriptive statistics

Standard deviation164589.56
Coefficient of variation (CV)0.60474305
Kurtosis-1.4036824
Mean272164.45
Median Absolute Deviation (MAD)140166
Skewness0.05347714
Sum3.9322864 × 1010
Variance2.7089724 × 1010
MonotonicityNot monotonic
2025-12-06T17:11:50.853719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5601014
 
< 0.1%
5601004
 
< 0.1%
5600994
 
< 0.1%
5600984
 
< 0.1%
5600964
 
< 0.1%
5600954
 
< 0.1%
5600944
 
< 0.1%
5600934
 
< 0.1%
5600924
 
< 0.1%
5600904
 
< 0.1%
Other values (40969)144442
> 99.9%
ValueCountFrequency (%)
100014
< 0.1%
100024
< 0.1%
100034
< 0.1%
100044
< 0.1%
100054
< 0.1%
100064
< 0.1%
100074
< 0.1%
100084
< 0.1%
100094
< 0.1%
100104
< 0.1%
ValueCountFrequency (%)
5601212
< 0.1%
5601202
< 0.1%
5601192
< 0.1%
5601183
< 0.1%
5601173
< 0.1%
5601163
< 0.1%
5601153
< 0.1%
5601143
< 0.1%
5601133
< 0.1%
5601123
< 0.1%

STATENAME
Categorical

High cardinality  High correlation 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size146.2 KiB
California
14857 
Texas
14447 
Florida
12367 
Georgia
 
5954
North Carolina
 
5629
Other values (46)
91228 

Length

Max length20
Median length13
Mean length8.2966944
Min length4

Characters and Unicode

Total characters1198723
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlabama
2nd rowAlabama
3rd rowAlabama
4th rowAlabama
5th rowAlabama

Common Values

ValueCountFrequency (%)
California14857
 
10.3%
Texas14447
 
10.0%
Florida12367
 
8.6%
Georgia5954
 
4.1%
North Carolina5629
 
3.9%
Tennessee4447
 
3.1%
Ohio4364
 
3.0%
Pennsylvania4318
 
3.0%
Arizona4221
 
2.9%
Illinois4179
 
2.9%
Other values (41)69699
48.2%

Length

2025-12-06T17:11:50.958261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california14857
 
9.0%
texas14447
 
8.8%
florida12367
 
7.5%
carolina9520
 
5.8%
new8224
 
5.0%
north6007
 
3.7%
georgia5954
 
3.6%
tennessee4447
 
2.7%
ohio4364
 
2.7%
virginia4346
 
2.6%
Other values (45)79978
48.6%

Most occurring characters

ValueCountFrequency (%)
a165377
13.8%
i139487
11.6%
o101750
 
8.5%
n100092
 
8.3%
s77864
 
6.5%
r77824
 
6.5%
e71242
 
5.9%
l61126
 
5.1%
C28123
 
2.3%
h27473
 
2.3%
Other values (36)348365
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1198723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a165377
13.8%
i139487
11.6%
o101750
 
8.5%
n100092
 
8.3%
s77864
 
6.5%
r77824
 
6.5%
e71242
 
5.9%
l61126
 
5.1%
C28123
 
2.3%
h27473
 
2.3%
Other values (36)348365
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1198723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a165377
13.8%
i139487
11.6%
o101750
 
8.5%
n100092
 
8.3%
s77864
 
6.5%
r77824
 
6.5%
e71242
 
5.9%
l61126
 
5.1%
C28123
 
2.3%
h27473
 
2.3%
Other values (36)348365
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1198723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a165377
13.8%
i139487
11.6%
o101750
 
8.5%
n100092
 
8.3%
s77864
 
6.5%
r77824
 
6.5%
e71242
 
5.9%
l61126
 
5.1%
C28123
 
2.3%
h27473
 
2.3%
Other values (36)348365
29.1%

PERMVIT
Real number (ℝ)

High correlation 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2112997
Minimum0
Maximum128
Zeros134
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size423.4 KiB
2025-12-06T17:11:51.022366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum128
Range128
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7473306
Coefficient of variation (CV)0.79018264
Kurtosis263.97602
Mean2.2112997
Median Absolute Deviation (MAD)1
Skewness7.2210273
Sum319493
Variance3.0531643
MonotonicityNot monotonic
2025-12-06T17:11:51.088411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
161623
42.7%
240200
27.8%
320724
 
14.3%
410131
 
7.0%
55326
 
3.7%
62833
 
2.0%
71499
 
1.0%
8799
 
0.6%
9449
 
0.3%
10265
 
0.2%
Other values (34)633
 
0.4%
ValueCountFrequency (%)
0134
 
0.1%
161623
42.7%
240200
27.8%
320724
 
14.3%
410131
 
7.0%
55326
 
3.7%
62833
 
2.0%
71499
 
1.0%
8799
 
0.6%
9449
 
0.3%
ValueCountFrequency (%)
1281
 
< 0.1%
741
 
< 0.1%
571
 
< 0.1%
521
 
< 0.1%
473
< 0.1%
464
< 0.1%
451
 
< 0.1%
442
< 0.1%
421
 
< 0.1%
401
 
< 0.1%

PERNOTMVIT
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25110394
Minimum0
Maximum73
Zeros111306
Zeros (%)77.0%
Negative0
Negative (%)0.0%
Memory size282.3 KiB
2025-12-06T17:11:51.140672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum73
Range73
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.54833918
Coefficient of variation (CV)2.1837139
Kurtosis2208.4897
Mean0.25110394
Median Absolute Deviation (MAD)0
Skewness19.815925
Sum36280
Variance0.30067586
MonotonicityNot monotonic
2025-12-06T17:11:51.190927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0111306
77.0%
131167
 
21.6%
21475
 
1.0%
3307
 
0.2%
4123
 
0.1%
552
 
< 0.1%
621
 
< 0.1%
711
 
< 0.1%
87
 
< 0.1%
94
 
< 0.1%
Other values (7)9
 
< 0.1%
ValueCountFrequency (%)
0111306
77.0%
131167
 
21.6%
21475
 
1.0%
3307
 
0.2%
4123
 
0.1%
552
 
< 0.1%
621
 
< 0.1%
711
 
< 0.1%
87
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
731
 
< 0.1%
231
 
< 0.1%
201
 
< 0.1%
191
 
< 0.1%
181
 
< 0.1%
121
 
< 0.1%
103
 
< 0.1%
94
 
< 0.1%
87
< 0.1%
711
< 0.1%

VE_TOTAL
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5836229
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size282.3 KiB
2025-12-06T17:11:51.241389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum59
Range58
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.85001077
Coefficient of variation (CV)0.53675075
Kurtosis298.4772
Mean1.5836229
Median Absolute Deviation (MAD)0
Skewness7.5787234
Sum228805
Variance0.72251831
MonotonicityNot monotonic
2025-12-06T17:11:51.292565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
178585
54.4%
253564
37.1%
38917
 
6.2%
42117
 
1.5%
5766
 
0.5%
6277
 
0.2%
7117
 
0.1%
858
 
< 0.1%
926
 
< 0.1%
1019
 
< 0.1%
Other values (16)36
 
< 0.1%
ValueCountFrequency (%)
178585
54.4%
253564
37.1%
38917
 
6.2%
42117
 
1.5%
5766
 
0.5%
6277
 
0.2%
7117
 
0.1%
858
 
< 0.1%
926
 
< 0.1%
1019
 
< 0.1%
ValueCountFrequency (%)
591
< 0.1%
501
< 0.1%
352
< 0.1%
341
< 0.1%
281
< 0.1%
271
< 0.1%
241
< 0.1%
212
< 0.1%
201
< 0.1%
191
< 0.1%

VE_FORMS
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5433964
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size282.3 KiB
2025-12-06T17:11:51.344524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum59
Range58
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80903565
Coefficient of variation (CV)0.52419174
Kurtosis359.62801
Mean1.5433964
Median Absolute Deviation (MAD)0
Skewness8.2891808
Sum222993
Variance0.65453869
MonotonicityNot monotonic
2025-12-06T17:11:51.396963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
181590
56.5%
252156
36.1%
37967
 
5.5%
41759
 
1.2%
5601
 
0.4%
6212
 
0.1%
795
 
0.1%
840
 
< 0.1%
919
 
< 0.1%
1015
 
< 0.1%
Other values (16)28
 
< 0.1%
ValueCountFrequency (%)
181590
56.5%
252156
36.1%
37967
 
5.5%
41759
 
1.2%
5601
 
0.4%
6212
 
0.1%
795
 
0.1%
840
 
< 0.1%
919
 
< 0.1%
1015
 
< 0.1%
ValueCountFrequency (%)
591
< 0.1%
501
< 0.1%
352
< 0.1%
341
< 0.1%
281
< 0.1%
271
< 0.1%
241
< 0.1%
211
< 0.1%
201
< 0.1%
191
< 0.1%

PVH_INVL
Real number (ℝ)

Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.040226464
Minimum0
Maximum20
Zeros140430
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size282.3 KiB
2025-12-06T17:11:51.445967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29080262
Coefficient of variation (CV)7.2291369
Kurtosis359.71421
Mean0.040226464
Median Absolute Deviation (MAD)0
Skewness13.537184
Sum5812
Variance0.084566163
MonotonicityNot monotonic
2025-12-06T17:11:51.497081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0140430
97.2%
13016
 
2.1%
2651
 
0.5%
3224
 
0.2%
488
 
0.1%
534
 
< 0.1%
616
 
< 0.1%
710
 
< 0.1%
106
 
< 0.1%
93
 
< 0.1%
Other values (3)4
 
< 0.1%
ValueCountFrequency (%)
0140430
97.2%
13016
 
2.1%
2651
 
0.5%
3224
 
0.2%
488
 
0.1%
534
 
< 0.1%
616
 
< 0.1%
710
 
< 0.1%
82
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
201
 
< 0.1%
111
 
< 0.1%
106
 
< 0.1%
93
 
< 0.1%
82
 
< 0.1%
710
 
< 0.1%
616
 
< 0.1%
534
 
< 0.1%
488
 
0.1%
3224
0.2%

MONTH
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7012915
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size282.3 KiB
2025-12-06T17:11:51.544848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3682169
Coefficient of variation (CV)0.50262205
Kurtosis-1.1438495
Mean6.7012915
Median Absolute Deviation (MAD)3
Skewness-0.089903347
Sum968216
Variance11.344885
MonotonicityNot monotonic
2025-12-06T17:11:51.592525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1013287
9.2%
913174
9.1%
813103
9.1%
713060
9.0%
612522
8.7%
512509
8.7%
1111933
8.3%
1211922
8.3%
411210
7.8%
311001
7.6%
Other values (2)20761
14.4%
ValueCountFrequency (%)
110833
7.5%
29928
6.9%
311001
7.6%
411210
7.8%
512509
8.7%
612522
8.7%
713060
9.0%
813103
9.1%
913174
9.1%
1013287
9.2%
ValueCountFrequency (%)
1211922
8.3%
1111933
8.3%
1013287
9.2%
913174
9.1%
813103
9.1%
713060
9.0%
612522
8.7%
512509
8.7%
411210
7.8%
311001
7.6%

DAY
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.627324
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size282.3 KiB
2025-12-06T17:11:51.640534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8316787
Coefficient of variation (CV)0.56514338
Kurtosis-1.1985066
Mean15.627324
Median Absolute Deviation (MAD)8
Skewness0.020099704
Sum2257867
Variance77.998548
MonotonicityNot monotonic
2025-12-06T17:11:51.695601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
45020
 
3.5%
15017
 
3.5%
34940
 
3.4%
154894
 
3.4%
64794
 
3.3%
224788
 
3.3%
94785
 
3.3%
54783
 
3.3%
214779
 
3.3%
74771
 
3.3%
Other values (21)95911
66.4%
ValueCountFrequency (%)
15017
3.5%
24706
3.3%
34940
3.4%
45020
3.5%
54783
3.3%
64794
3.3%
74771
3.3%
84755
3.3%
94785
3.3%
104678
3.2%
ValueCountFrequency (%)
312688
1.9%
304564
3.2%
294392
3.0%
284674
3.2%
274607
3.2%
264636
3.2%
254567
3.2%
244725
3.3%
234705
3.3%
224788
3.3%

DAY_WEEKNAME
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size141.9 KiB
Saturday
25100 
Sunday
22338 
Friday
22329 
Thursday
19229 
Monday
18837 
Other values (2)
36649 

Length

Max length9
Median length8
Mean length7.123524
Min length6

Characters and Unicode

Total characters1029221
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFriday
2nd rowMonday
3rd rowMonday
4th rowTuesday
5th rowFriday

Common Values

ValueCountFrequency (%)
Saturday25100
17.4%
Sunday22338
15.5%
Friday22329
15.5%
Thursday19229
13.3%
Monday18837
13.0%
Wednesday18511
12.8%
Tuesday18138
12.6%

Length

2025-12-06T17:11:51.769806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T17:11:51.819775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
saturday25100
17.4%
sunday22338
15.5%
friday22329
15.5%
thursday19229
13.3%
monday18837
13.0%
wednesday18511
12.8%
tuesday18138
12.6%

Most occurring characters

ValueCountFrequency (%)
a169582
16.5%
d162993
15.8%
y144482
14.0%
u84805
8.2%
r66658
 
6.5%
n59686
 
5.8%
s55878
 
5.4%
e55160
 
5.4%
S47438
 
4.6%
T37367
 
3.6%
Other values (7)145172
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1029221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a169582
16.5%
d162993
15.8%
y144482
14.0%
u84805
8.2%
r66658
 
6.5%
n59686
 
5.8%
s55878
 
5.4%
e55160
 
5.4%
S47438
 
4.6%
T37367
 
3.6%
Other values (7)145172
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1029221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a169582
16.5%
d162993
15.8%
y144482
14.0%
u84805
8.2%
r66658
 
6.5%
n59686
 
5.8%
s55878
 
5.4%
e55160
 
5.4%
S47438
 
4.6%
T37367
 
3.6%
Other values (7)145172
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1029221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a169582
16.5%
d162993
15.8%
y144482
14.0%
u84805
8.2%
r66658
 
6.5%
n59686
 
5.8%
s55878
 
5.4%
e55160
 
5.4%
S47438
 
4.6%
T37367
 
3.6%
Other values (7)145172
14.1%

HOUR
Real number (ℝ)

Zeros 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.520452
Minimum0
Maximum99
Zeros5671
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size282.3 KiB
2025-12-06T17:11:51.880070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median14
Q319
95-th percentile23
Maximum99
Range99
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.078241
Coefficient of variation (CV)0.74540706
Kurtosis34.969315
Mean13.520452
Median Absolute Deviation (MAD)6
Skewness4.218842
Sum1953462
Variance101.57093
MonotonicityNot monotonic
2025-12-06T17:11:51.933047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
218540
 
5.9%
208514
 
5.9%
188451
 
5.8%
198128
 
5.6%
177788
 
5.4%
227364
 
5.1%
167222
 
5.0%
157071
 
4.9%
146615
 
4.6%
236428
 
4.4%
Other values (15)68361
47.3%
ValueCountFrequency (%)
05671
3.9%
15316
3.7%
25429
3.8%
33970
2.7%
43638
2.5%
54764
3.3%
65426
3.8%
74739
3.3%
83912
2.7%
93957
2.7%
ValueCountFrequency (%)
991047
 
0.7%
236428
4.4%
227364
5.1%
218540
5.9%
208514
5.9%
198128
5.6%
188451
5.8%
177788
5.4%
167222
5.0%
157071
4.9%

MINUTE
Real number (ℝ)

Zeros 

Distinct61
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.206697
Minimum0
Maximum99
Zeros6865
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size282.3 KiB
2025-12-06T17:11:51.992822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114
median30
Q345
95-th percentile56
Maximum99
Range99
Interquartile range (IQR)31

Descriptive statistics

Standard deviation18.372396
Coefficient of variation (CV)0.62904737
Kurtosis-0.022999575
Mean29.206697
Median Absolute Deviation (MAD)15
Skewness0.31790202
Sum4219842
Variance337.54493
MonotonicityNot monotonic
2025-12-06T17:11:52.061957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06865
 
4.8%
306065
 
4.2%
504655
 
3.2%
454471
 
3.1%
204464
 
3.1%
404406
 
3.0%
154397
 
3.0%
104083
 
2.8%
353686
 
2.6%
253679
 
2.5%
Other values (51)97711
67.6%
ValueCountFrequency (%)
06865
4.8%
11806
 
1.2%
21844
 
1.3%
31867
 
1.3%
41876
 
1.3%
53391
2.3%
61779
 
1.2%
71837
 
1.3%
82030
 
1.4%
91789
 
1.2%
ValueCountFrequency (%)
991048
 
0.7%
591679
1.2%
582062
1.4%
571895
1.3%
561810
1.3%
553514
2.4%
541889
1.3%
531949
1.3%
521924
1.3%
511721
1.2%
Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.3 MiB
2025-12-06T17:11:52.188238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length133
Median length60
Mean length18.814351
Min length4

Characters and Unicode

Total characters2718335
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowParked Motor Vehicle
2nd rowFence
3rd rowGuardrail Face
4th rowGuardrail Face
5th rowMotor Vehicle In-Transport
ValueCountFrequency (%)
vehicle60620
18.8%
motor59247
18.4%
in-transport57218
17.8%
pedestrian25547
 
7.9%
rollover/overturn10406
 
3.2%
tree9792
 
3.0%
standing9792
 
3.0%
only9792
 
3.0%
support5326
 
1.7%
curb4545
 
1.4%
Other values (106)69813
21.7%
2025-12-06T17:11:52.377704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r286526
 
10.5%
e255814
 
9.4%
o222850
 
8.2%
t203068
 
7.5%
n202667
 
7.5%
177616
 
6.5%
i133483
 
4.9%
a126367
 
4.6%
l119757
 
4.4%
s93131
 
3.4%
Other values (45)897056
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2718335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r286526
 
10.5%
e255814
 
9.4%
o222850
 
8.2%
t203068
 
7.5%
n202667
 
7.5%
177616
 
6.5%
i133483
 
4.9%
a126367
 
4.6%
l119757
 
4.4%
s93131
 
3.4%
Other values (45)897056
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2718335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r286526
 
10.5%
e255814
 
9.4%
o222850
 
8.2%
t203068
 
7.5%
n202667
 
7.5%
177616
 
6.5%
i133483
 
4.9%
a126367
 
4.6%
l119757
 
4.4%
s93131
 
3.4%
Other values (45)897056
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2718335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r286526
 
10.5%
e255814
 
9.4%
o222850
 
8.2%
t203068
 
7.5%
n202667
 
7.5%
177616
 
6.5%
i133483
 
4.9%
a126367
 
4.6%
l119757
 
4.4%
s93131
 
3.4%
Other values (45)897056
33.0%

MAN_COLL
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9185712
Minimum0
Maximum99
Zeros87097
Zeros (%)60.3%
Negative0
Negative (%)0.0%
Memory size282.3 KiB
2025-12-06T17:11:52.436151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum99
Range99
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.8702851
Coefficient of variation (CV)3.0597171
Kurtosis217.92351
Mean1.9185712
Median Absolute Deviation (MAD)0
Skewness13.484143
Sum277199
Variance34.460247
MonotonicityNot monotonic
2025-12-06T17:11:52.482729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
087097
60.3%
626425
 
18.3%
215799
 
10.9%
110353
 
7.2%
72311
 
1.6%
81554
 
1.1%
11362
 
0.3%
99250
 
0.2%
98184
 
0.1%
9145
 
0.1%
ValueCountFrequency (%)
087097
60.3%
110353
 
7.2%
215799
 
10.9%
626425
 
18.3%
72311
 
1.6%
81554
 
1.1%
9145
 
0.1%
102
 
< 0.1%
11362
 
0.3%
98184
 
0.1%
ValueCountFrequency (%)
99250
 
0.2%
98184
 
0.1%
11362
 
0.3%
102
 
< 0.1%
9145
 
0.1%
81554
 
1.1%
72311
 
1.6%
626425
18.3%
215799
10.9%
110353
 
7.2%

MAN_COLLNAME
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.1 MiB
The First Harmful Event was Not a Collision with a Motor Vehicle in Transport
66529 
Angle
26425 
Not a Collision with Motor Vehicle In-Transport
20568 
Front-to-Front
15799 
Front-to-Rear
10353 
Other values (7)
 
4808

Length

Max length77
Median length47
Mean length46.334955
Min length5

Characters and Unicode

Total characters6694567
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot a Collision with Motor Vehicle In-Transport
2nd rowNot a Collision with Motor Vehicle In-Transport
3rd rowNot a Collision with Motor Vehicle In-Transport
4th rowNot a Collision with Motor Vehicle In-Transport
5th rowFront-to-Rear

Common Values

ValueCountFrequency (%)
The First Harmful Event was Not a Collision with a Motor Vehicle in Transport66529
46.0%
Angle26425
 
18.3%
Not a Collision with Motor Vehicle In-Transport20568
 
14.2%
Front-to-Front15799
 
10.9%
Front-to-Rear10353
 
7.2%
Sideswipe - Same Direction2311
 
1.6%
Sideswipe - Opposite Direction1554
 
1.1%
Other362
 
0.3%
Reported as Unknown250
 
0.2%
Not Reported184
 
0.1%
Other values (2)147
 
0.1%

Length

2025-12-06T17:11:52.538988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a153626
13.4%
not87281
 
7.6%
with87097
 
7.6%
collision87097
 
7.6%
vehicle87097
 
7.6%
motor87097
 
7.6%
first66529
 
5.8%
the66529
 
5.8%
event66529
 
5.8%
harmful66529
 
5.8%
Other values (18)289635
25.3%

Most occurring characters

ValueCountFrequency (%)
1000564
14.9%
o597119
 
8.9%
t556095
 
8.3%
i498605
 
7.4%
r451463
 
6.7%
n400811
 
6.0%
a386844
 
5.8%
e361014
 
5.4%
l354245
 
5.3%
s312921
 
4.7%
Other values (27)1774886
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)6694567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1000564
14.9%
o597119
 
8.9%
t556095
 
8.3%
i498605
 
7.4%
r451463
 
6.7%
n400811
 
6.0%
a386844
 
5.8%
e361014
 
5.4%
l354245
 
5.3%
s312921
 
4.7%
Other values (27)1774886
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6694567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1000564
14.9%
o597119
 
8.9%
t556095
 
8.3%
i498605
 
7.4%
r451463
 
6.7%
n400811
 
6.0%
a386844
 
5.8%
e361014
 
5.4%
l354245
 
5.3%
s312921
 
4.7%
Other values (27)1774886
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6694567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1000564
14.9%
o597119
 
8.9%
t556095
 
8.3%
i498605
 
7.4%
r451463
 
6.7%
n400811
 
6.0%
a386844
 
5.8%
e361014
 
5.4%
l354245
 
5.3%
s312921
 
4.7%
Other values (27)1774886
26.5%

RELJCT2NAME
Categorical

Imbalance 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.8 KiB
Non-Junction
95816 
Intersection
22882 
Intersection-Related
13613 
Driveway Access Related
 
4458
Entrance/Exit Ramp Related
 
2242
Other values (10)
 
5471

Length

Max length38
Median length12
Mean length13.684321
Min length12

Characters and Unicode

Total characters1977138
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon-Junction
2nd rowOther location within Interchange Area
3rd rowNon-Junction
4th rowNon-Junction
5th rowNon-Junction

Common Values

ValueCountFrequency (%)
Non-Junction95816
66.3%
Intersection22882
 
15.8%
Intersection-Related13613
 
9.4%
Driveway Access Related4458
 
3.1%
Entrance/Exit Ramp Related2242
 
1.6%
Through Roadway1887
 
1.3%
Other location within Interchange Area1339
 
0.9%
Entrance/Exit Ramp522
 
0.4%
Driveway Access508
 
0.4%
Railway Grade Crossing475
 
0.3%
Other values (5)740
 
0.5%

Length

2025-12-06T17:11:52.604177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
non-junction95816
57.1%
intersection22882
 
13.6%
intersection-related13613
 
8.1%
related6700
 
4.0%
driveway4966
 
3.0%
access4966
 
3.0%
entrance/exit2764
 
1.6%
ramp2764
 
1.6%
through1887
 
1.1%
roadway1887
 
1.1%
Other values (17)9598
 
5.7%

Most occurring characters

ValueCountFrequency (%)
n372608
18.8%
o236497
12.0%
t201027
10.2%
c147889
 
7.5%
i145181
 
7.3%
e134124
 
6.8%
-109695
 
5.5%
u97703
 
4.9%
N96032
 
4.9%
J95816
 
4.8%
Other values (29)340566
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1977138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n372608
18.8%
o236497
12.0%
t201027
10.2%
c147889
 
7.5%
i145181
 
7.3%
e134124
 
6.8%
-109695
 
5.5%
u97703
 
4.9%
N96032
 
4.9%
J95816
 
4.8%
Other values (29)340566
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1977138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n372608
18.8%
o236497
12.0%
t201027
10.2%
c147889
 
7.5%
i145181
 
7.3%
e134124
 
6.8%
-109695
 
5.5%
u97703
 
4.9%
N96032
 
4.9%
J95816
 
4.8%
Other values (29)340566
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1977138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n372608
18.8%
o236497
12.0%
t201027
10.2%
c147889
 
7.5%
i145181
 
7.3%
e134124
 
6.8%
-109695
 
5.5%
u97703
 
4.9%
N96032
 
4.9%
J95816
 
4.8%
Other values (29)340566
17.2%

ROUTE
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6908127
Minimum0
Maximum99
Zeros3519
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size282.3 KiB
2025-12-06T17:11:52.652673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile9
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation13.868948
Coefficient of variation (CV)2.4370769
Kurtosis37.151692
Mean5.6908127
Median Absolute Deviation (MAD)1
Skewness6.1732621
Sum822220
Variance192.34773
MonotonicityNot monotonic
2025-12-06T17:11:52.707202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
342150
29.2%
223728
16.4%
619082
13.2%
418091
12.5%
118015
12.5%
87629
 
5.3%
94786
 
3.3%
03519
 
2.4%
52910
 
2.0%
952668
 
1.8%
Other values (7)1904
 
1.3%
ValueCountFrequency (%)
03519
 
2.4%
118015
12.5%
223728
16.4%
342150
29.2%
418091
12.5%
52910
 
2.0%
619082
13.2%
7739
 
0.5%
87629
 
5.3%
94786
 
3.3%
ValueCountFrequency (%)
99512
 
0.4%
9691
 
0.1%
952668
 
1.8%
1316
 
< 0.1%
12525
 
0.4%
1112
 
< 0.1%
109
 
< 0.1%
94786
3.3%
87629
5.3%
7739
 
0.5%

FUNC_SYS
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2906729
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size282.3 KiB
2025-12-06T17:11:52.758816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile7
Maximum99
Range98
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.9194152
Coefficient of variation (CV)1.6126643
Kurtosis165.66465
Mean4.2906729
Median Absolute Deviation (MAD)1
Skewness12.549177
Sum619925
Variance47.878307
MonotonicityNot monotonic
2025-12-06T17:11:52.809049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
342861
29.7%
432652
22.6%
522857
15.8%
118042
12.5%
716318
 
11.3%
26176
 
4.3%
64826
 
3.3%
96375
 
0.3%
98252
 
0.2%
99123
 
0.1%
ValueCountFrequency (%)
118042
12.5%
26176
 
4.3%
342861
29.7%
432652
22.6%
522857
15.8%
64826
 
3.3%
716318
 
11.3%
96375
 
0.3%
98252
 
0.2%
99123
 
0.1%
ValueCountFrequency (%)
99123
 
0.1%
98252
 
0.2%
96375
 
0.3%
716318
 
11.3%
64826
 
3.3%
522857
15.8%
432652
22.6%
342861
29.7%
26176
 
4.3%
118042
12.5%

FUNC_SYSNAME
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Minor Arterial
32652 
Principal Arterial - Other
31489 
Major Collector
22857 
Interstate
18042 
Local
16318 
Other values (7)
23124 

Length

Max length51
Median length30
Mean length17.481638
Min length5

Characters and Unicode

Total characters2525782
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInterstate
2nd rowInterstate
3rd rowInterstate
4th rowInterstate
5th rowInterstate

Common Values

ValueCountFrequency (%)
Minor Arterial32652
22.6%
Principal Arterial - Other31489
21.8%
Major Collector22857
15.8%
Interstate18042
12.5%
Local16318
11.3%
Other Principal Arterial11372
 
7.9%
Minor Collector4826
 
3.3%
Principal Arterial - Other Freeways and Expressways4677
 
3.2%
Other Freeways and Expressways1499
 
1.0%
Trafficway Not in State Inventory375
 
0.3%
Other values (2)375
 
0.3%

Length

2025-12-06T17:11:52.872620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arterial80190
22.5%
other49037
13.8%
principal47538
13.3%
minor37478
10.5%
36166
10.1%
collector27683
 
7.8%
major22857
 
6.4%
interstate18042
 
5.1%
local16318
 
4.6%
freeways6176
 
1.7%
Other values (9)14854
 
4.2%

Most occurring characters

ValueCountFrequency (%)
r376369
14.9%
i213494
 
8.5%
t213040
 
8.4%
e212776
 
8.4%
211857
 
8.4%
a204598
 
8.1%
l199412
 
7.9%
o133396
 
5.3%
n110728
 
4.4%
c91914
 
3.6%
Other values (26)558198
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2525782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r376369
14.9%
i213494
 
8.5%
t213040
 
8.4%
e212776
 
8.4%
211857
 
8.4%
a204598
 
8.1%
l199412
 
7.9%
o133396
 
5.3%
n110728
 
4.4%
c91914
 
3.6%
Other values (26)558198
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2525782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r376369
14.9%
i213494
 
8.5%
t213040
 
8.4%
e212776
 
8.4%
211857
 
8.4%
a204598
 
8.1%
l199412
 
7.9%
o133396
 
5.3%
n110728
 
4.4%
c91914
 
3.6%
Other values (26)558198
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2525782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r376369
14.9%
i213494
 
8.5%
t213040
 
8.4%
e212776
 
8.4%
211857
 
8.4%
a204598
 
8.1%
l199412
 
7.9%
o133396
 
5.3%
n110728
 
4.4%
c91914
 
3.6%
Other values (26)558198
22.1%

RUR_URBNAME
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size141.7 KiB
Urban
84280 
Rural
59558 
Trafficway Not in State Inventory
 
375
Not Reported
 
166
Unknown
 
103

Length

Max length33
Median length5
Mean length5.0821417
Min length5

Characters and Unicode

Total characters734278
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRural
2nd rowUrban
3rd rowRural
4th rowRural
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban84280
58.3%
Rural59558
41.2%
Trafficway Not in State Inventory375
 
0.3%
Not Reported166
 
0.1%
Unknown103
 
0.1%

Length

2025-12-06T17:11:52.934889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T17:11:52.977862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
urban84280
57.7%
rural59558
40.8%
not541
 
0.4%
trafficway375
 
0.3%
in375
 
0.3%
state375
 
0.3%
inventory375
 
0.3%
reported166
 
0.1%
unknown103
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a144963
19.7%
r144754
19.7%
n85714
11.7%
U84383
11.5%
b84280
11.5%
R59724
8.1%
u59558
8.1%
l59558
8.1%
t1832
 
0.2%
1666
 
0.2%
Other values (15)7846
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)734278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a144963
19.7%
r144754
19.7%
n85714
11.7%
U84383
11.5%
b84280
11.5%
R59724
8.1%
u59558
8.1%
l59558
8.1%
t1832
 
0.2%
1666
 
0.2%
Other values (15)7846
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)734278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a144963
19.7%
r144754
19.7%
n85714
11.7%
U84383
11.5%
b84280
11.5%
R59724
8.1%
u59558
8.1%
l59558
8.1%
t1832
 
0.2%
1666
 
0.2%
Other values (15)7846
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)734278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a144963
19.7%
r144754
19.7%
n85714
11.7%
U84383
11.5%
b84280
11.5%
R59724
8.1%
u59558
8.1%
l59558
8.1%
t1832
 
0.2%
1666
 
0.2%
Other values (15)7846
 
1.1%

TYP_INTNAME
Categorical

Imbalance 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.3 MiB
Not an Intersection
107637 
Four-Way Intersection
22790 
T-Intersection
12265 
Y-Intersection
 
759
Not Reported
 
418
Other values (6)
 
613

Length

Max length23
Median length19
Mean length18.834201
Min length10

Characters and Unicode

Total characters2721203
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot an Intersection
2nd rowNot an Intersection
3rd rowNot an Intersection
4th rowNot an Intersection
5th rowNot an Intersection

Common Values

ValueCountFrequency (%)
Not an Intersection107637
74.5%
Four-Way Intersection22790
 
15.8%
T-Intersection12265
 
8.5%
Y-Intersection759
 
0.5%
Not Reported418
 
0.3%
Reported as Unknown184
 
0.1%
Five Point, or More179
 
0.1%
Roundabout116
 
0.1%
L-Intersection75
 
0.1%
Traffic Circle34
 
< 0.1%

Length

2025-12-06T17:11:53.040892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
intersection130452
34.0%
not108055
28.1%
an107637
28.0%
four-way22790
 
5.9%
t-intersection12265
 
3.2%
y-intersection759
 
0.2%
reported602
 
0.2%
as184
 
< 0.1%
unknown184
 
< 0.1%
five179
 
< 0.1%
Other values (9)846
 
0.2%

Most occurring characters

ValueCountFrequency (%)
t396079
14.6%
n395586
14.5%
e288748
10.6%
o275951
10.1%
239471
8.8%
r167394
6.2%
i143977
 
5.3%
s143735
 
5.3%
c143619
 
5.3%
I143551
 
5.3%
Other values (26)383092
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2721203
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t396079
14.6%
n395586
14.5%
e288748
10.6%
o275951
10.1%
239471
8.8%
r167394
6.2%
i143977
 
5.3%
s143735
 
5.3%
c143619
 
5.3%
I143551
 
5.3%
Other values (26)383092
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2721203
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t396079
14.6%
n395586
14.5%
e288748
10.6%
o275951
10.1%
239471
8.8%
r167394
6.2%
i143977
 
5.3%
s143735
 
5.3%
c143619
 
5.3%
I143551
 
5.3%
Other values (26)383092
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2721203
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t396079
14.6%
n395586
14.5%
e288748
10.6%
o275951
10.1%
239471
8.8%
r167394
6.2%
i143977
 
5.3%
s143735
 
5.3%
c143619
 
5.3%
I143551
 
5.3%
Other values (26)383092
14.1%

REL_ROADNAME
Categorical

Imbalance 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.6 KiB
On Roadway
91422 
On Roadside
40338 
On Median
 
5126
Outside Trafficway
 
2640
On Shoulder
 
2199
Other values (8)
 
2757

Length

Max length42
Median length10
Mean length10.520169
Min length4

Characters and Unicode

Total characters1519975
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOn Shoulder
2nd rowOn Median
3rd rowOn Roadside
4th rowOn Roadside
5th rowOn Roadway

Common Values

ValueCountFrequency (%)
On Roadway91422
63.3%
On Roadside40338
27.9%
On Median5126
 
3.5%
Outside Trafficway2640
 
1.8%
On Shoulder2199
 
1.5%
Gore776
 
0.5%
In Parking Lane/Zone438
 
0.3%
Separator405
 
0.3%
Off Roadway-Location Unknown402
 
0.3%
Reported as Unknown221
 
0.2%
Other values (3)515
 
0.4%

Length

2025-12-06T17:11:53.096466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
on139085
48.0%
roadway91422
31.5%
roadside40338
 
13.9%
median5126
 
1.8%
outside2640
 
0.9%
trafficway2640
 
0.9%
shoulder2199
 
0.8%
gore776
 
0.3%
unknown623
 
0.2%
in438
 
0.2%
Other values (18)4681
 
1.6%

Most occurring characters

ValueCountFrequency (%)
a237457
15.6%
d183303
12.1%
n149399
9.8%
145486
9.6%
O142127
9.4%
o138515
9.1%
R132714
8.7%
w95087
6.3%
y94464
 
6.2%
e54118
 
3.6%
Other values (25)147305
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1519975
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a237457
15.6%
d183303
12.1%
n149399
9.8%
145486
9.6%
O142127
9.4%
o138515
9.1%
R132714
8.7%
w95087
6.3%
y94464
 
6.2%
e54118
 
3.6%
Other values (25)147305
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1519975
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a237457
15.6%
d183303
12.1%
n149399
9.8%
145486
9.6%
O142127
9.4%
o138515
9.1%
R132714
8.7%
w95087
6.3%
y94464
 
6.2%
e54118
 
3.6%
Other values (25)147305
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1519975
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a237457
15.6%
d183303
12.1%
n149399
9.8%
145486
9.6%
O142127
9.4%
o138515
9.1%
R132714
8.7%
w95087
6.3%
y94464
 
6.2%
e54118
 
3.6%
Other values (25)147305
9.7%

WRK_ZONENAME
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.1%
Missing141395
Missing (%)97.9%
Memory size141.6 KiB
Construction
1867 
Work Zone, Type Unknown
1000 
Maintenance
 
186
Utility
 
34

Length

Max length23
Median length12
Mean length15.448008
Min length7

Characters and Unicode

Total characters47688
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConstruction
2nd rowConstruction
3rd rowConstruction
4th rowMaintenance
5th rowWork Zone, Type Unknown

Common Values

ValueCountFrequency (%)
Construction1867
 
1.3%
Work Zone, Type Unknown1000
 
0.7%
Maintenance186
 
0.1%
Utility34
 
< 0.1%
(Missing)141395
97.9%

Length

2025-12-06T17:11:53.154195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T17:11:53.190320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
construction1867
30.7%
work1000
16.4%
zone1000
16.4%
type1000
16.4%
unknown1000
16.4%
maintenance186
 
3.1%
utility34
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n8292
17.4%
o6734
14.1%
t3988
 
8.4%
3000
 
6.3%
r2867
 
6.0%
e2372
 
5.0%
i2121
 
4.4%
c2053
 
4.3%
k2000
 
4.2%
C1867
 
3.9%
Other values (13)12394
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)47688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n8292
17.4%
o6734
14.1%
t3988
 
8.4%
3000
 
6.3%
r2867
 
6.0%
e2372
 
5.0%
i2121
 
4.4%
c2053
 
4.3%
k2000
 
4.2%
C1867
 
3.9%
Other values (13)12394
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)47688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n8292
17.4%
o6734
14.1%
t3988
 
8.4%
3000
 
6.3%
r2867
 
6.0%
e2372
 
5.0%
i2121
 
4.4%
c2053
 
4.3%
k2000
 
4.2%
C1867
 
3.9%
Other values (13)12394
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)47688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n8292
17.4%
o6734
14.1%
t3988
 
8.4%
3000
 
6.3%
r2867
 
6.0%
e2372
 
5.0%
i2121
 
4.4%
c2053
 
4.3%
k2000
 
4.2%
C1867
 
3.9%
Other values (13)12394
26.0%

SCH_BUSNAME
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size141.4 KiB
No
144064 
Yes
 
418

Length

Max length3
Median length2
Mean length2.0028931
Min length2

Characters and Unicode

Total characters289382
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No144064
99.7%
Yes418
 
0.3%

Length

2025-12-06T17:11:53.243044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T17:11:53.277104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no144064
99.7%
yes418
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N144064
49.8%
o144064
49.8%
Y418
 
0.1%
e418
 
0.1%
s418
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)289382
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N144064
49.8%
o144064
49.8%
Y418
 
0.1%
e418
 
0.1%
s418
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)289382
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N144064
49.8%
o144064
49.8%
Y418
 
0.1%
e418
 
0.1%
s418
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)289382
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N144064
49.8%
o144064
49.8%
Y418
 
0.1%
e418
 
0.1%
s418
 
0.1%

RAIL
Text

Distinct517
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
2025-12-06T17:11:53.439659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1011374
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique506 ?
Unique (%)0.4%

Sample

1st row0000000
2nd row0000000
3rd row0000000
4th row0000000
5th row0000000
ValueCountFrequency (%)
0000000143934
99.6%
999999924
 
< 0.1%
028442l2
 
< 0.1%
390521p2
 
< 0.1%
764083b2
 
< 0.1%
796261y2
 
< 0.1%
667324w2
 
< 0.1%
155637w2
 
< 0.1%
352088w2
 
< 0.1%
608311k2
 
< 0.1%
Other values (507)508
 
0.4%
2025-12-06T17:11:53.693718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
01007899
99.7%
9401
 
< 0.1%
3363
 
< 0.1%
4338
 
< 0.1%
2333
 
< 0.1%
5331
 
< 0.1%
7312
 
< 0.1%
6307
 
< 0.1%
8301
 
< 0.1%
1265
 
< 0.1%
Other values (22)524
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1011374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01007899
99.7%
9401
 
< 0.1%
3363
 
< 0.1%
4338
 
< 0.1%
2333
 
< 0.1%
5331
 
< 0.1%
7312
 
< 0.1%
6307
 
< 0.1%
8301
 
< 0.1%
1265
 
< 0.1%
Other values (22)524
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1011374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01007899
99.7%
9401
 
< 0.1%
3363
 
< 0.1%
4338
 
< 0.1%
2333
 
< 0.1%
5331
 
< 0.1%
7312
 
< 0.1%
6307
 
< 0.1%
8301
 
< 0.1%
1265
 
< 0.1%
Other values (22)524
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1011374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01007899
99.7%
9401
 
< 0.1%
3363
 
< 0.1%
4338
 
< 0.1%
2333
 
< 0.1%
5331
 
< 0.1%
7312
 
< 0.1%
6307
 
< 0.1%
8301
 
< 0.1%
1265
 
< 0.1%
Other values (22)524
 
0.1%

LGT_CONDNAME
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.1 KiB
Daylight
66267 
Dark - Not Lighted
39258 
Dark - Lighted
30332 
Dusk
 
3457
Dawn
 
2795
Other values (4)
 
2373

Length

Max length23
Median length19
Mean length12.003454
Min length4

Characters and Unicode

Total characters1734283
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDawn
2nd rowDark - Not Lighted
3rd rowDark - Not Lighted
4th rowDaylight
5th rowDaylight

Common Values

ValueCountFrequency (%)
Daylight66267
45.9%
Dark - Not Lighted39258
27.2%
Dark - Lighted30332
21.0%
Dusk3457
 
2.4%
Dawn2795
 
1.9%
Dark - Unknown Lighting1376
 
1.0%
Reported as Unknown671
 
0.5%
Not Reported260
 
0.2%
Other66
 
< 0.1%

Length

2025-12-06T17:11:53.784287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T17:11:53.836989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
dark70966
21.6%
70966
21.6%
lighted69590
21.2%
daylight66267
20.2%
not39518
12.0%
dusk3457
 
1.1%
dawn2795
 
0.9%
unknown2047
 
0.6%
lighting1376
 
0.4%
reported931
 
0.3%
Other values (2)737
 
0.2%

Most occurring characters

ValueCountFrequency (%)
184168
10.6%
t177748
 
10.2%
D143485
 
8.3%
a140699
 
8.1%
g138609
 
8.0%
i138609
 
8.0%
h137299
 
7.9%
k76470
 
4.4%
r71963
 
4.1%
e71518
 
4.1%
Other values (15)453715
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1734283
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
184168
10.6%
t177748
 
10.2%
D143485
 
8.3%
a140699
 
8.1%
g138609
 
8.0%
i138609
 
8.0%
h137299
 
7.9%
k76470
 
4.4%
r71963
 
4.1%
e71518
 
4.1%
Other values (15)453715
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1734283
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
184168
10.6%
t177748
 
10.2%
D143485
 
8.3%
a140699
 
8.1%
g138609
 
8.0%
i138609
 
8.0%
h137299
 
7.9%
k76470
 
4.4%
r71963
 
4.1%
e71518
 
4.1%
Other values (15)453715
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1734283
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
184168
10.6%
t177748
 
10.2%
D143485
 
8.3%
a140699
 
8.1%
g138609
 
8.0%
i138609
 
8.0%
h137299
 
7.9%
k76470
 
4.4%
r71963
 
4.1%
e71518
 
4.1%
Other values (15)453715
26.2%

WEATHERNAME
Categorical

Imbalance 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size142.5 KiB
Clear
102752 
Cloudy
19482 
Rain
10443 
Not Reported
 
7415
Fog, Smog, Smoke
 
1514
Other values (8)
 
2876

Length

Max length24
Median length5
Mean length5.6516521
Min length4

Characters and Unicode

Total characters816562
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear
2nd rowRain
3rd rowCloudy
4th rowCloudy
5th rowClear

Common Values

ValueCountFrequency (%)
Clear102752
71.1%
Cloudy19482
 
13.5%
Rain10443
 
7.2%
Not Reported7415
 
5.1%
Fog, Smog, Smoke1514
 
1.0%
Snow1319
 
0.9%
Reported as Unknown696
 
0.5%
Severe Crosswinds219
 
0.2%
Sleet or Hail197
 
0.1%
Other150
 
0.1%
Other values (3)295
 
0.2%

Length

2025-12-06T17:11:53.909692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clear102752
65.2%
cloudy19482
 
12.4%
rain10561
 
6.7%
reported8111
 
5.1%
not7415
 
4.7%
fog1514
 
1.0%
smog1514
 
1.0%
smoke1514
 
1.0%
snow1459
 
0.9%
as696
 
0.4%
Other values (13)2517
 
1.6%

Most occurring characters

ValueCountFrequency (%)
l122960
15.1%
C122453
15.0%
e122043
14.9%
a114243
14.0%
r112039
13.7%
o42453
 
5.2%
d27849
 
3.4%
u19482
 
2.4%
y19482
 
2.4%
R18672
 
2.3%
Other values (22)94886
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)816562
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l122960
15.1%
C122453
15.0%
e122043
14.9%
a114243
14.0%
r112039
13.7%
o42453
 
5.2%
d27849
 
3.4%
u19482
 
2.4%
y19482
 
2.4%
R18672
 
2.3%
Other values (22)94886
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)816562
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l122960
15.1%
C122453
15.0%
e122043
14.9%
a114243
14.0%
r112039
13.7%
o42453
 
5.2%
d27849
 
3.4%
u19482
 
2.4%
y19482
 
2.4%
R18672
 
2.3%
Other values (22)94886
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)816562
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l122960
15.1%
C122453
15.0%
e122043
14.9%
a114243
14.0%
r112039
13.7%
o42453
 
5.2%
d27849
 
3.4%
u19482
 
2.4%
y19482
 
2.4%
R18672
 
2.3%
Other values (22)94886
11.6%

FATALS
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0853394
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size282.3 KiB
2025-12-06T17:11:53.978037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum20
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35105484
Coefficient of variation (CV)0.32345168
Kurtosis102.72701
Mean1.0853394
Median Absolute Deviation (MAD)0
Skewness6.5131132
Sum156812
Variance0.1232395
MonotonicityNot monotonic
2025-12-06T17:11:54.039332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1134403
93.0%
28438
 
5.8%
31219
 
0.8%
4308
 
0.2%
578
 
0.1%
621
 
< 0.1%
76
 
< 0.1%
86
 
< 0.1%
92
 
< 0.1%
201
 
< 0.1%
ValueCountFrequency (%)
1134403
93.0%
28438
 
5.8%
31219
 
0.8%
4308
 
0.2%
578
 
0.1%
621
 
< 0.1%
76
 
< 0.1%
86
 
< 0.1%
92
 
< 0.1%
201
 
< 0.1%
ValueCountFrequency (%)
201
 
< 0.1%
92
 
< 0.1%
86
 
< 0.1%
76
 
< 0.1%
621
 
< 0.1%
578
 
0.1%
4308
 
0.2%
31219
 
0.8%
28438
 
5.8%
1134403
93.0%

YEAR
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.3 MiB
2022
39422 
2023
37654 
2018
33919 
2019
33487 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters577928
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
202239422
27.3%
202337654
26.1%
201833919
23.5%
201933487
23.2%

Length

2025-12-06T17:11:54.109883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-06T17:11:54.157300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
202239422
27.3%
202337654
26.1%
201833919
23.5%
201933487
23.2%

Most occurring characters

ValueCountFrequency (%)
2260980
45.2%
0144482
25.0%
167406
 
11.7%
337654
 
6.5%
833919
 
5.9%
933487
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)577928
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2260980
45.2%
0144482
25.0%
167406
 
11.7%
337654
 
6.5%
833919
 
5.9%
933487
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)577928
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2260980
45.2%
0144482
25.0%
167406
 
11.7%
337654
 
6.5%
833919
 
5.9%
933487
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)577928
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2260980
45.2%
0144482
25.0%
167406
 
11.7%
337654
 
6.5%
833919
 
5.9%
933487
 
5.8%

Interactions

2025-12-06T17:11:47.252148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:32.207918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:35.204634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:36.237158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.220435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.149826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.068811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:40.028500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.312161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.287970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.264223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.262238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.190740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:46.109214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:47.337387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:32.290352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:35.280679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:36.313259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.293577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.212013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.133059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:40.090365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.383245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.349473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.354373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.334660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.258003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:46.204593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:47.420063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:32.356372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:35.345733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:36.388311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.365711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.275984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.202201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:40.154646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.451867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.428970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.423949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.404987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.324134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:46.274355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:47.504473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:32.429637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:35.416382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:36.457586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.432814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.348290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.267986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:40.222583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.514661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.498275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.498013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.480718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.394194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:46.348181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:47.585616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:32.506528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-06T17:11:37.510231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.416845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.339378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:40.286644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.582744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-06T17:11:43.565901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-06T17:11:39.408755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:40.347363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.651420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.649231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.643602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.603278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.523177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:46.522526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:47.787897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:32.643126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:35.710799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:36.667211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.640886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.555915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.480256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:40.430423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.721149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.720792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.718414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.682549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.593396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:46.617038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:47.871010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:32.706334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:35.776608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:36.736012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.704299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.616002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.556582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:40.489671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.787339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.781855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.780266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.741878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.668839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:46.695380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:47.943858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:32.769953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:35.850848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:36.805425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.765857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.683119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.623142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:40.554301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.848695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.843506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.852784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.804434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.735998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:46.768512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:48.018069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:32.838733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:35.916237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:36.874664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.826560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.743990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.688007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:40.624290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.953225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.917967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.923309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.865075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.793090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:46.841322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:48.091344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:32.908320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:35.978882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:36.947990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.891236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.807855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.754019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.061799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.037971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.986594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.991571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.934045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.856280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:46.920255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:48.166342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:32.988046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:36.043429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.021003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.956370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.872863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.823062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.122109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.095616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.054696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.058737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.991508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.919656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:47.004954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:48.240609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:35.072870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:36.106327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.088573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.019636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.937426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.889904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.181478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.156967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.117197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.123227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.056385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.987215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:47.088584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:48.308658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:35.137817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:36.171180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:37.153136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.080727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:38.996820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:39.956125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:41.242285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:42.222813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:43.179333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:44.188542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:45.119595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:46.047215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-06T17:11:47.170210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-06T17:11:54.224344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
DAYDAY_WEEKNAMEFATALSFUNC_SYSFUNC_SYSNAMEHOURLGT_CONDNAMEMAN_COLLMAN_COLLNAMEMINUTEMONTHPERMVITPERNOTMVITPVH_INVLRELJCT2NAMEREL_ROADNAMEROUTERUR_URBNAMESCH_BUSNAMESTATENAMEST_CASETYP_INTNAMEVE_FORMSVE_TOTALWEATHERNAMEWRK_ZONENAMEYEAR
DAY1.0000.0190.001-0.0010.0000.0010.0000.0020.0010.0010.0070.002-0.006-0.0010.0030.0000.0030.0040.0000.0050.0030.0030.0020.0010.0090.0000.007
DAY_WEEKNAME0.0191.0000.0060.0000.0160.0530.0430.0070.0270.0050.0220.0030.0030.0010.0230.0350.0040.0070.0330.0140.0100.0180.0050.0040.0110.0280.008
FATALS0.0010.0061.000-0.0420.013-0.0140.0000.1300.040-0.001-0.0020.275-0.1050.0060.0110.008-0.0510.0200.0060.0090.0080.0040.1470.1450.0030.0110.003
FUNC_SYS-0.0010.000-0.0421.0001.0000.0690.095-0.0910.141-0.0000.010-0.141-0.064-0.0220.1540.1540.6060.7860.0010.1030.0330.144-0.177-0.1800.0940.0340.031
FUNC_SYSNAME0.0000.0160.0131.0001.0000.0840.0820.1290.1310.0350.0130.0180.0250.0070.1640.1470.1280.6420.0170.1010.0600.1250.0190.0160.0570.1010.339
HOUR0.0010.053-0.0140.0690.0841.0000.441-0.0030.1240.0220.0100.0130.083-0.0250.1250.1100.0650.0770.0250.100-0.0090.097-0.002-0.0100.1180.0210.010
LGT_CONDNAME0.0000.0430.0000.0950.0820.4411.0000.1490.1260.1290.0550.0020.0000.0130.1790.1520.0370.1980.0430.1080.0460.1560.0020.0030.1940.0620.022
MAN_COLL0.0020.0070.130-0.0910.129-0.0030.1491.0001.0000.005-0.0080.650-0.389-0.0590.2960.283-0.0690.1200.0020.1050.0040.2790.8590.8160.1550.0000.005
MAN_COLLNAME0.0010.0270.0400.1410.1310.1240.1261.0001.0000.0510.0090.0260.0000.0050.2750.2350.0600.1310.0380.0630.0300.2230.0310.0280.0900.0580.449
MINUTE0.0010.005-0.001-0.0000.0350.0220.1290.0050.0511.000-0.001-0.0020.003-0.0080.0610.0690.0050.0410.0020.0610.0030.0570.0040.0020.0720.0000.000
MONTH0.0070.022-0.0020.0100.0130.0100.055-0.0080.009-0.0011.000-0.0050.019-0.0010.0130.0100.0040.0230.0210.0280.0500.005-0.008-0.0080.0590.0430.005
PERMVIT0.0020.0030.275-0.1410.0180.0130.0020.6500.026-0.002-0.0051.000-0.334-0.0430.0030.011-0.1170.0000.0880.0090.0060.0000.7500.7170.0470.0000.006
PERNOTMVIT-0.0060.003-0.105-0.0640.0250.0830.000-0.3890.0000.0030.019-0.3341.0000.1090.0220.0040.0680.0250.0000.014-0.0660.000-0.350-0.3110.0001.0000.000
PVH_INVL-0.0010.0010.006-0.0220.007-0.0250.013-0.0590.005-0.008-0.001-0.0430.1091.0000.0170.063-0.0050.0140.0010.006-0.0010.000-0.0590.2200.0090.0000.000
RELJCT2NAME0.0030.0230.0110.1540.1640.1250.1790.2960.2750.0610.0130.0030.0220.0171.0000.3300.0380.1880.0230.0820.0510.4690.0060.0090.1110.0270.027
REL_ROADNAME0.0000.0350.0080.1540.1470.1100.1520.2830.2350.0690.0100.0110.0040.0630.3301.0000.0390.1690.0330.0900.0560.3070.0020.0030.1040.0300.023
ROUTE0.0030.004-0.0510.6060.1280.0650.037-0.0690.0600.0050.004-0.1170.068-0.0050.0380.0391.0000.0820.0000.258-0.0630.036-0.131-0.1310.0200.0000.197
RUR_URBNAME0.0040.0070.0200.7860.6420.0770.1980.1200.1310.0410.0230.0000.0250.0140.1880.1690.0821.0000.0000.1760.0980.1700.0030.0070.0960.0260.037
SCH_BUSNAME0.0000.0330.0060.0010.0170.0250.0430.0020.0380.0020.0210.0880.0000.0010.0230.0330.0000.0001.0000.0260.0150.0210.0060.0080.0090.0000.002
STATENAME0.0050.0140.0090.1030.1010.1000.1080.1050.0630.0610.0280.0090.0140.0060.0820.0900.2580.1760.0261.0000.9920.0830.0110.0120.2290.4050.017
ST_CASE0.0030.0100.0080.0330.060-0.0090.0460.0040.0300.0030.0500.006-0.066-0.0010.0510.056-0.0630.0980.0150.9921.0000.0410.0050.0040.1460.2230.005
TYP_INTNAME0.0030.0180.0040.1440.1250.0970.1560.2790.2230.0570.0050.0000.0000.0000.4690.3070.0360.1700.0210.0830.0411.0000.0000.0000.1210.0240.020
VE_FORMS0.0020.0050.147-0.1770.019-0.0020.0020.8590.0310.004-0.0080.750-0.350-0.0590.0060.002-0.1310.0030.0060.0110.0050.0001.0000.9550.0510.0430.003
VE_TOTAL0.0010.0040.145-0.1800.016-0.0100.0030.8160.0280.002-0.0080.717-0.3110.2200.0090.003-0.1310.0070.0080.0120.0040.0000.9551.0000.0510.0430.000
WEATHERNAME0.0090.0110.0030.0940.0570.1180.1940.1550.0900.0720.0590.0470.0000.0090.1110.1040.0200.0960.0090.2290.1460.1210.0510.0511.0000.0720.061
WRK_ZONENAME0.0000.0280.0110.0340.1010.0210.0620.0000.0580.0000.0430.0001.0000.0000.0270.0300.0000.0260.0000.4050.2230.0240.0430.0430.0721.0000.043
YEAR0.0070.0080.0030.0310.3390.0100.0220.0050.4490.0000.0050.0060.0000.0000.0270.0230.1970.0370.0020.0170.0050.0200.0030.0000.0610.0431.000

Missing values

2025-12-06T17:11:49.105129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-06T17:11:49.583904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ST_CASESTATENAMEPERMVITPERNOTMVITVE_TOTALVE_FORMSPVH_INVLMONTHDAYDAY_WEEKNAMEHOURMINUTEHARM_EVNAMEMAN_COLLMAN_COLLNAMERELJCT2NAMEROUTEFUNC_SYSFUNC_SYSNAMERUR_URBNAMETYP_INTNAMEREL_ROADNAMEWRK_ZONENAMESCH_BUSNAMERAILLGT_CONDNAMEWEATHERNAMEFATALSYEAR
010001Alabama1021115Friday60Parked Motor Vehicle0Not a Collision with Motor Vehicle In-TransportNon-Junction11InterstateRuralNot an IntersectionOn Shoulder<NA>No0000000DawnClear12018
110002Alabama2011018Monday048Fence0Not a Collision with Motor Vehicle In-TransportOther location within Interchange Area11InterstateUrbanNot an IntersectionOn Median<NA>No0000000Dark - Not LightedRain22018
210003Alabama2022018Monday2250Guardrail Face0Not a Collision with Motor Vehicle In-TransportNon-Junction11InterstateRuralNot an IntersectionOn RoadsideConstructionNo0000000Dark - Not LightedCloudy12018
310004Alabama2011019Tuesday132Guardrail Face0Not a Collision with Motor Vehicle In-TransportNon-Junction11InterstateRuralNot an IntersectionOn Roadside<NA>No0000000DaylightCloudy12018
410005Alabama20220119Friday79Motor Vehicle In-Transport1Front-to-RearNon-Junction11InterstateUrbanNot an IntersectionOn Roadway<NA>No0000000DaylightClear12018
510006Alabama14211119Friday228Parked Motor Vehicle0Not a Collision with Motor Vehicle In-TransportNon-Junction11InterstateRuralNot an IntersectionOn Shoulder<NA>No0000000Dark - Not LightedClear12018
610007Alabama11110121Sunday913Pedestrian0Not a Collision with Motor Vehicle In-TransportNon-Junction11InterstateUrbanNot an IntersectionOn Roadway<NA>No0000000DaylightClear12018
710008Alabama11220123Tuesday032Motor Vehicle In-Transport1Front-to-RearNon-Junction11InterstateUrbanNot an IntersectionOn Roadway<NA>No0000000Dark - Not LightedCloudy12018
810009Alabama10110127Saturday035Traffic Sign Support0Not a Collision with Motor Vehicle In-TransportEntrance/Exit Ramp Related11InterstateUrbanNot an IntersectionGore<NA>No0000000Dark - LightedCloudy12018
910010Alabama11110128Sunday2215Pedestrian0Not a Collision with Motor Vehicle In-TransportNon-Junction11InterstateUrbanNot an IntersectionOn RoadwayConstructionNo0000000Dark - Not LightedClear12018
ST_CASESTATENAMEPERMVITPERNOTMVITVE_TOTALVE_FORMSPVH_INVLMONTHDAYDAY_WEEKNAMEHOURMINUTEHARM_EVNAMEMAN_COLLMAN_COLLNAMERELJCT2NAMEROUTEFUNC_SYSFUNC_SYSNAMERUR_URBNAMETYP_INTNAMEREL_ROADNAMEWRK_ZONENAMESCH_BUSNAMERAILLGT_CONDNAMEWEATHERNAMEFATALSYEAR
144472560112Wyoming101101129Wednesday740Ditch0The First Harmful Event was Not a Collision with a Motor Vehicle in TransportNon-Junction35Major CollectorRuralNot an IntersectionOn Roadside<NA>No0000000DaylightClear12023
144473560113Wyoming302201130Thursday647Motor Vehicle In-Transport6AngleNon-Junction23Other Principal ArterialRuralNot an IntersectionOn Roadway<NA>No0000000DawnClear22023
144474560114Wyoming10110122Saturday725Post, Pole or Other Supports0The First Harmful Event was Not a Collision with a Motor Vehicle in TransportNon-Junction33Other Principal ArterialUrbanNot an IntersectionOn Roadside<NA>No0000000DaylightClear12023
144475560115Wyoming111101217Sunday1817Pedalcyclist0The First Harmful Event was Not a Collision with a Motor Vehicle in TransportNon-Junction33Other Principal ArterialUrbanNot an IntersectionOn Roadway<NA>No0000000Dark - Not LightedClear12023
144476560116Wyoming211101221Thursday1810Pedestrian0The First Harmful Event was Not a Collision with a Motor Vehicle in TransportNon-Junction33Other Principal ArterialUrbanNot an IntersectionOn Roadway<NA>No0000000Dark - Not LightedClear12023
144477560117Wyoming605501223Saturday1934Motor Vehicle In-Transport2Front-to-FrontNon-Junction33Other Principal ArterialRuralNot an IntersectionOn Roadway<NA>No0000000Dark - Not LightedBlowing Snow12023
144478560118Wyoming101101226Tuesday2345Rollover/Overturn0The First Harmful Event was Not a Collision with a Motor Vehicle in TransportNon-Junction23Other Principal ArterialRuralNot an IntersectionOn Roadside<NA>No0000000Dark - Not LightedClear12023
144479560119Wyoming111101227Wednesday950Pedestrian0The First Harmful Event was Not a Collision with a Motor Vehicle in TransportNon-Junction11InterstateRuralNot an IntersectionOn Roadway<NA>No0000000DaylightClear12023
144480560120Wyoming602201229Friday836Motor Vehicle In-Transport6AngleNon-Junction23Other Principal ArterialRuralNot an IntersectionOn Roadway<NA>No0000000DaylightClear12023
144481560121Wyoming101101231Sunday152Rollover/Overturn0The First Harmful Event was Not a Collision with a Motor Vehicle in TransportNon-Junction45Major CollectorRuralNot an IntersectionOn Roadside<NA>No0000000DaylightClear12023